On the Bike Spreading Problem

• URL: http://arxiv.org/abs/2107.00761v1
• Date: Thu, 1 Jul 2021 22:14:31 GMT
• Title: On the Bike Spreading Problem
• Authors: Elia Costa and Francesco Silvestri
• Abstract summary: A free-floating bike-sharing system (FFBSS) is a dockless rental system where an individual can borrow a bike and returns it everywhere. We show that it is possible to position batches of bikes on a small number of zones, and then the daily use of FFBSS will efficiently spread these bikes on a large area.
• Score: 1.4467794332678539
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A free-floating bike-sharing system (FFBSS) is a dockless rental system where an individual can borrow a bike and returns it everywhere, within the service area. To improve the rental service, available bikes should be distributed over the entire service area: a customer leaving from any position is then more likely to find a near bike and then to use the service. Moreover, spreading bikes among the entire service area increases urban spatial equity since the benefits of FFBSS are not a prerogative of just a few zones. For guaranteeing such distribution, the FFBSS operator can use vans to manually relocate bikes, but it incurs high economic and environmental costs. We propose a novel approach that exploits the existing bike flows generated by customers to distribute bikes. More specifically, by envisioning the problem as an Influence Maximization problem, we show that it is possible to position batches of bikes on a small number of zones, and then the daily use of FFBSS will efficiently spread these bikes on a large area. We show that detecting these areas is NP-complete, but there exists a simple and efficient $1-1/e$ approximation algorithm; our approach is then evaluated on a dataset of rides from the free-floating bike-sharing system of the city of Padova.

Related papers

• Micromobility Flow Prediction: A Bike Sharing Station-level Study via Multi-level Spatial-Temporal Attention Neural Network [14.73426609677324]
  We propose BikeMAN, a multi-level-temporal attention neural network to predict station-level bike traffic for entire bike sharing systems.<n>Our network showed high accuracy in predicting the bike station traffic of all stations in New York City.
  arXiv  Detail & Related papers  (2025-07-21T19:31:42Z)
• Evaluating the effects of Data Sparsity on the Link-level Bicycling Volume Estimation: A Graph Convolutional Neural Network Approach [54.84957282120537]
  We present the first study to utilize a Graph Convolutional Network architecture to model link-level bicycling volumes. We estimate the Annual Average Daily Bicycle (AADB) counts across the City of Melbourne, Australia using Strava Metro bicycling count data. Our results show that the GCN model performs better than these traditional models in predicting AADB counts.
  arXiv  Detail & Related papers  (2024-10-11T04:53:18Z)
• Mining individual daily commuting patterns of dockless bike-sharing users: a two-layer framework integrating spatiotemporal flow clustering and rule-based decision trees [3.3988622291971247]
  This paper presents a framework to mine individual cyclists' daily home-work commuting patterns from dockless bike-sharing trip data with user IDs. The effectiveness and applicability of the framework is demonstrated by over 200 million bike-sharing trip records in Shenzhen. Lots of bike-sharing commuters live near urban villages and old communities with lower costs of living, especially in the central city.
  arXiv  Detail & Related papers  (2024-07-13T09:30:51Z)
• Smart Recommendations for Renting Bikes in Bike Sharing Systems [1.5115914900997285]
  Vehicle-sharing systems have become increasingly popular in big cities in recent years. One of their advantages is their availability, e.g., the possibility of taking (or leaving) a vehicle almost anywhere in a city. Agglutination problems -- where, due to usage patterns, available vehicles are concentrated in certain areas, whereas no vehicles are available in others -- are quite common in such systems. We present and compare strategies for recommending stations to users who wish to rent or return bikes in station-based bike-sharing systems.
  arXiv  Detail & Related papers  (2024-01-22T19:29:33Z)
• The Growth of E-Bike Use: A Machine Learning Approach [57.506876852412034]
  E-bike usage in the U.S. resulted in a reduction of 15,737.82 kilograms of CO2 emissions in 2022. E-bike users burned approximately 716,630.727 kilocalories through their activities in the same year.
  arXiv  Detail & Related papers  (2023-07-15T03:34:10Z)
• FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy [84.45004766136663]
  Federated learning is an emerging distributed machine learning framework. It suffers from the non-vanishing biases introduced by the local inconsistent optimal and the rugged client-drifts by the local over-fitting. We propose a novel and practical method, FedSpeed, to alleviate the negative impacts posed by these problems.
  arXiv  Detail & Related papers  (2023-02-21T03:55:29Z)
• Predicting Citi Bike Demand Evolution Using Dynamic Graphs [81.12174591442479]
  We apply a graph neural network model to predict bike demand in the New York City, Citi Bike dataset. In this paper, we attempt to apply a graph neural network model to predict bike demand in the New York City, Citi Bike dataset.
  arXiv  Detail & Related papers  (2022-12-18T21:43:27Z)
• Transfer Learning Approach to Bicycle-sharing Systems' Station Location Planning using OpenStreetMap Data [4.869953137750582]
  This paper proposes a new solution to streamline and facilitate the process of such planning by using spatial embedding methods. Based on publicly available data from OpenStreetMap, and station layouts from 34 cities in Europe, a method has been developed to divide cities into micro-regions.
  arXiv  Detail & Related papers  (2021-11-01T14:56:49Z)
• Dynamic Bicycle Dispatching of Dockless Public Bicycle-sharing Systems using Multi-objective Reinforcement Learning [79.61517670541863]
  How to use AI to provide efficient bicycle dispatching solutions based on dynamic bicycle rental demand is an essential issue for dockless PBS (DL-PBS) We propose a dynamic bicycle dispatching algorithm based on multi-objective reinforcement learning (MORL-BD) to provide the optimal bicycle dispatching solution for DL-PBS.
  arXiv  Detail & Related papers  (2021-01-19T03:09:51Z)
• Dynamic Planning of Bicycle Stations in Dockless Public Bicycle-sharing System Using Gated Graph Neural Network [79.61517670541863]
  Dockless Public Bicycle-sharing (DL-PBS) network becomes increasingly popular in many countries. redundant and low-utility stations waste public urban space and maintenance costs of DL-PBS vendors. We propose a Bicycle Station Dynamic Planning (BSDP) system to dynamically provide the optimal bicycle station layout for the DL-PBS network.
  arXiv  Detail & Related papers  (2021-01-19T02:51:12Z)
• Exploring the weather impact on bike sharing usage through a clustering analysis [7.541020519717183]
  This study aims to explore how and in what magnitude weather impacts bike usage in Washington D.C. Bike usage data and weather data are gathered for the city of Washington D.C. and are analyzed using k-means clustering algorithm.
  arXiv  Detail & Related papers  (2020-08-17T12:24:37Z)
• Simulating and Evaluating Rebalancing Strategies for Dockless Bike-Sharing Systems [0.0]
  Dockless bike sharing systems are revolutionizing the market for the increased flexibility they offer to users. Bike redistribution is a common approach to improve service, and there exists extensive research considering static and dynamic rebalancing strategies for dock-based systems. This paper offers an optimizing Mixed Program framework to model the effects of various bike repositioning strategies for dockless systems.
  arXiv  Detail & Related papers  (2020-04-24T07:13:56Z)
• Exploiting Interpretable Patterns for Flow Prediction in Dockless Bike Sharing Systems [45.45179250456602]
  We propose an Interpretable Bike Flow Prediction (IBFP) framework, which can provide effective bike flow prediction with interpretable traffic patterns. By dividing the urban area into regions according to flow density, we first model the bike flows between regions with graph regularized sparse representation. Then, we extract traffic patterns from bike flows using subspace clustering with sparse representation to construct interpretable base matrices. Finally, experimental results on real-world data show the advantages of the IBFP method for flow prediction in dockless bike sharing systems.
  arXiv  Detail & Related papers  (2020-04-13T05:31:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.